ANOVA (and similar) Tests for Mean/Median Differences

Compute and return information on tests for the homogeneity of data.

Usage

anova_tests(data, tests = c("anova", "welch", "bayes", "kruskal", "median"))

Arguments

data

Data.frame with the first column the values and the second column the group names

tests

Vector of strings, or a string, indicating the tests to check. Options include 'anova', 'welch', 'bayes', 'kruskal', and 'median'

Value

A list of means, medians, and anova test results

Details

Tests require independent data unless otherwise specified. anova: Classic ANOVA. Requires normal data with homogeneous variance and similar group sizes welch: ANOVA modification that allows nonhomogeneous variances and is more robust to varying group sizes bayes: Bayesian approach to group differences kruskal: Also called the Krukal-wallis test. Non-parametric test median: Also called Brown-Mood median test. Non-parametric test with Bonferroni correction

See also

stats::aov(), stats::oneway.test(), BayesFactor::anovaBF(), stats::kruskal.test(), PMCMRplus::medianTest()

Examples

data <- data.frame(
  "value" = c(rnorm(14, sd = 2), rnorm(6), rnorm(20, mean = 2)),
  "group" = c(rep("A", 14), rep("B", 6), rep("C", 20))
)
anova_tests(data)
#> $means
#>          A          B          C 
#> -0.5577610 -0.3827129  2.0143038 
#> 
#> $medians
#>          A          B          C 
#> -0.4915249 -0.2873072  2.0480439 
#> 
#> $anova
#> [1] 0.000125744
#> 
#> $welch
#> [1] 0.0001171815
#> 
#> $bayes
#> [1] 273.893
#> 
#> $kruskal
#> [1] 0.000219581
#> 
#> $median
#> [1] 0.0003760779
#>